October 28, 2025 • 26 mins
If you’re out there being told to slap AI tools onto everything and call it “digital transformation,” this episode is your reality check. I sat down with Darren Murph—yes, the remote‑work oracle behind GitLab’s all‑remote strategy—to pull back the curtain on what needs to exist before you ever type “chatbot” or “LLM integration” into your roadmap.
We dug into why good documentation isn’t optional anymore, why remote‑work lessons are now directly relevant to AI adoption, and how companies who rushed ahead without building infrastructure are setting themselves up for a trust disaster. In short: if your data, your knowledge systems, your culture aren’t ready for AI, this technology is not your solution—it’s your liability.
Related Links:
- Join the People Managing People community forum
- Subscribe to the newsletter to get our latest articles and podcasts
- Connect with Darren on LinkedIn
- Check out Darren’s website
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
David Rice (00:00):
A lot of people out there, they're
rushing to adopt AI.
What foundational workdo organizations need
to do before layering AIinto existing systems?
Darren Murph (00:10):
I see a lot of organizations rushing to
layer it over whatever theycurrently have, looking for
sparks of efficiency withoutstepping back and building a
great knowledge infrastructure,creating a one star
experience for all employees.
David Rice (00:25):
How does AI make documentation hygiene
even more essentialfor trust and adoption?
Darren Murph (00:30):
In a distributed world, you trust information
even more than people.
If AI leads you astray fouror five times in a row,
you're going to be much lesstrusting of that technology,
not just that day, butevery day going forward.
David Rice (00:44):
What specific lessons from distributed work
do you see kind of carryingover into how companies
will successfully adopt AI?
Darren Murph (00:52):
Two major first principles — transparency,
and asynchronous work flows.
The more that you've shiftedyour culture into a writing
culture, the better off youare now that we are moving from
the remote era to the AI era.
I would encourage leadersto take a step back and
make sure that you havethe right foundations in
project management andknowledge management.
David Rice (01:18):
Welcome to The People Managing People
Podcast — the show wherewe help leaders keep work
human in the era of AI.
I'm your host, David Rice.
And on today's show I'mjoined by Darren Murph.
Now you may know himas the go-to guy on
all things remote work.
He's the former head ofremote at GitLab and the
founder of Page 52 Consulting.
We're gonna be chatting abouthow AI is inherently a remote
(01:42):
work tool and how you can use itto build remote work cultures.
So Darren, welcome!
Darren Murph (01:46):
Hey, thanks for having me.
David Rice (01:47):
Alright, so just wanna jump right into it.
You know, a lot of people areout there, they're rushing
to adopt AI and of courseit's everybody's talking
like garbage in, garbage out.
That rule always applies.
Certainly applies here.
What foundational work doyou think organizations
need to do before layeringAI into existing systems,
especially when we think aboutremote work environments?
Darren Murph (02:10):
Yeah, this is a great question.
I see a lot of organizationsrushing to adopt AI and layer
it over whatever they currentlyhave, and then they're looking
for sparks of efficiencyor movement or some sort of
silver bullet for the future.
But I would encourage leadersto take a step back, take a
pause, and make sure that youhave the right foundations
(02:32):
in project managementand knowledge management.
And the knowledge oneis the most important.
So I wanna share twoexamples to kind of paint the
picture of what I mean here.
Imagine that your companyis a widget that anyone in
the world can buy on Amazon.
So it gets shipped to theirmailbox, they open it and
they shake it out, and yourwidget plops on the counter
and they say, how am Isupposed to operate this thing?
(02:55):
So then they look up inthe envelope and they shake
it some more expecting anoperating manual to fall out.
Now in the absence of anoperating manual, how do
you think people are goingto know how to use the
thing that you've built?
Well, a lot of trial and error.
This is kind of likeonboarding into most companies
right now that have notcodified the operating
rhythms of their company.
(03:15):
So what does this looklike in the age of AI?
Well, everything compounds,so if you have everything well
documented, that's going tocompound into AI having a lot of
great resources to extract from.
It compounds in theopposite direction too.
So imagine you're aticketing platform and
you sell concert tickets,and there's this upcoming
(03:35):
concert on your platform andtens of thousands of people
show up to buy tickets.
In addition to buyingtickets, they wanna use AI
to figure out what's the bestvalue for where I can sit.
And they also want to takethings into consideration,
like where is the sunlightgoing to be coming in at
the time of day that thisconcert is scheduled for?
And because I'm a baseplayer, I actually want to
(03:56):
know which seats should Ioptimize for based on where
the band will be set up.
Is the bass player going to beon stage left or stage right?
And many other things that youcould imagine AI helping with.
So imagine they show up to thisticketing site, but you, the
leader has built infrastructurethat has an old seating chart
that has an old incarnation ofthe band with the wrong layout.
(04:20):
Some of the seedsaren't even mapped yet.
So as a consumer trying to useAI to navigate this, you're
going to have an incrediblyfrustrating experience.
And it's likely that you'll notonly never come back to this
platform, but you'll find anyand all areas on the internet
to leave a one star review.
And so I worry that forleaders who are layering AI
(04:40):
now, without stepping backand building a great knowledge
infrastructure, you're actuallycreating a one star experience
for all of your employees.
David Rice (04:49):
That's a great analogy and I really liked,
you know, sort of thepicture that it paints.
And like remote work, youknow, this era has taught
companies a lot about theimportance of documentation
and single sources of truth.
But I'm curious, youknow, how does AI make
documentation, hygienemaybe even more essential
for trust and adoption?
Darren Murph (05:08):
We talk about trust as that's
the speed in which yourbusiness is going to run.
In a primarily co-locatedworld where most people go
to the same office to do worktogether, you really want to
be able to build trust with theother humans that you are in
close proximity to, but as theworld becomes more distributed.
You need to be ableto trust information
even more than people.
(05:29):
And this is especiallytrue in the age of AI.
I mean, you jokingly mentionedhallucinations, but seriously,
when you are engaging with AI,if it leads you astray four
or five times in a row, andyour reputation or the outcome
of a project depends on that.
You're going to be much lesstrusting of that technology,
not just that day, butevery day going forward.
(05:52):
And so, while documentationwas perhaps a nice to have
in the past, because youcould always use synchronous
meetings and verbalization topatch over communication gaps,
that's no longer true with AI.
You can't ask AI for input ona certain piece of information.
And then say whatever gaps youfind in the knowledge base,
(06:15):
go ask Tim and then come backwith your complete analysis.
He can't just phone Timup and fill in those gaps.
At least not yet.
And even if it could, Iwould say that's probably
not the most efficientway to scale information.
David Rice (06:28):
Well, you bring up an interesting point there.
Like there's a lot of baddata floating around in, I
would say, in the majority oforganizations, quite frankly.
And so if we're like looking atwhat information can you trust?
And what information canyou feed it and trust
that, I mean, what you'regonna get back is real.
I'd say we've got a lot ofwork to do on the back end
there to get ourselves ready.
(06:49):
Like how would you grademost organizations'
readiness in this area?
Darren Murph (06:54):
Great question.
So I work with organizationsall the time in trying to
build out this infrastructureand before we do any work, we
actually do an analysis andthe output of it puts them on
a curve and it never fails.
They think they're muchfurther along on the curve.
Then they actually are.
And so in most cases, theyare ready to adopt these
(07:15):
infrastructure improvements.
They recognize andsee the need of it.
They're not saying thatthis is unimportant, but
they have precious littleactually implemented.
And a telltale sign is do youtreat knowledge like a product?
Do you actually have someoneinternally that is either a
chief documentarian or a chieflibrarian, or do you have
a number of people that arerunning this as a product?
(07:38):
Think about securityin an organization.
This isn't somethingyou just wing.
Most organizations have achief security officer, or
they have systems in place.
They have whole teams inplace because you can't
assume that every employeejoining your company is going
to be a master of security.
The same is true withknowledge and documentation
unless you came up witha journalism degree.
(07:59):
Most people aren't trained inhow to think about taxonomy
and knowledge information, butin the age of AI, this is so
important within a company.
Most people who are doingChatGPT searches on the
internet, they can get greatresults because it's looking at
the entirety of the internet.
But when you scope thatdown to just what's
in your organization.
(08:20):
You can't go outsideof those walls.
It's on the company to saywe need to take knowledge as
seriously as we do security.
It's time to actually investin a knowledge base and put
some systems and potentiallypeople around building that out.
David Rice (08:35):
Yeah, it's interesting you
say that about org.
So it's like a lot of us, right?
We all think we're moreadvanced in our use of
AI than maybe we are.
So that's, it's veryfitting actually.
It extrapolates outto the org level.
I'm curious, the remote workwave that came with COVID,
it maybe was like the bestpreparation for this next era
of work that we could have had.
(08:55):
I'm wondering what specificlessons from distributed work
and that time that we're allforced into it, do you see,
kind of carrying over into howcompanies will successfully
adopt and deploy AI?
Darren Murph (09:07):
Two major first principles of remote
work that carry overwell are transparency and
asynchronous first workflows.
So companies quickly foundwhen they all went remote
that they had to be much moretransparent than before because
everyone needed informationand you couldn't all be in
the same place at all times.
And so you had to quicklyfigure out, how do we codify
(09:29):
all of this information that'sin our minds and make it
accessible and searchable?
This is paying dividends forcompanies that really invested
in that during COVID, now thatyou lay your AI on top of it.
Their output is much betterbecause they've already done
the upfront work of codifyingwhat was in people's minds.
The other part of this isasynchronous workflows.
(09:50):
I'm sure you've seen so muchdata over the pandemic of
the multiple hundreds ofpercentage point uptick in the
amount of teams meetings thatpeople engaged in, because
that was the only way thatthey knew to get work done,
and leaders quickly realizedthat this was not sustainable.
They implemented thingslike project management
tools, Asana, or Clickup.
(10:10):
These are great examples ofmoving work out of synchronous
meetings into tools thatare actually designed to
move large projects forward.
And so for organizations thatdid that, guess what all of
those projects that are codifiedin a tool instead of just being
out in the ether in a meeting.
Now AI can lookinto that as well.
(10:31):
So said another way.
The more that you've codified,the more that you've shifted
your culture into a writingculture, the better off you are.
Now that we are moving fromthe remote era to the AI era.
David Rice (10:45):
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It's so interesting every timeI hear you talk about this
and talking about a writingculture, it's funny 'cause I
think back to like, you know,when I was coming outta school
and people were like, ah, youknow, your skillset might not
be that valuable in 10 years.
(11:29):
And it turns out actually itturned out to be quite valuable.
It is actually sort of thekey, I think, to this next
chapter, which I'm grateful for.
Even companies callingemployees back to the office,
you know, a lot of 'em arestill distributed across
different cities and time zones.
How does AI kinda reinforcethe need for distributed
(11:50):
friendly practices?
Because even in this newreality, even when we're
co-located, we're stillseparate so much of the time,
hybrid in different cities.
So I, I'm curious whatyou think about that.
Darren Murph (12:01):
Yeah, look at it this way.
If you remove the physicaloffice from most multinationals,
business would go on, but ifyou removed the internet from
most multinationals, theirentire business would grind to
a halt probably within a week.
So which one is most important?
And so I chuckle a bit at thisbecause even organizations
that are making a huge dealabout enforcing and mandating
(12:24):
a return to office, unlessthey literally have one office
where everyone is literallyon one floor, they need to
embrace remote first principles.
There's been researchthat has done that.
Even if you're in oneskyscraper, the employee's
on floor seven and theemployee's on floor 41 almost
never engage with each otherin person, so they might
(12:45):
as well be oceans away.
And for most multinationals,they have multiple offices and
these span different culturesand languages and time zones.
And so whether they wantto identify as such or
not, the vast majority oforganizations in 2025 and
beyond are distributed.
And so ignoring thatreality just means that
they're falling further andfurther behind the curve.
(13:08):
When you think about how peopleengage with AI, even outside of
their organization, it is nota physical place that they go.
They don't get in a vehicle anddrive down to the local Lions
club in order to engage with AI.
They just use whateverdevice is in front of them.
And so the further that wego along, I would posit that
we are going to become a moredistributed working society,
(13:29):
not a less distributedworking society and AI is
only furthering that reality.
David Rice (13:35):
I feel like that's like the premise for a movie.
You know, you go down tothe Lions Club to like
talk to the AI, it's likezoltar in big, you know?
Darren Murph (13:42):
Yeah.
It's the new versionof the payphone.
Like we actually have to drivesomewhere to make a phone call.
I don't know, I think there'sa another season of The Wire.
David Rice (13:52):
I love it.
Some employees, when wethink about AI adoption, some
folks are fearing it, right?
And they see it as sortof like they're training
their replacement.
And I think it's a verylogical, sort of valid
concern for a lot of folks.
It seems to be sort of, we're inthis phase of shifting our value
away from tasks toward learning,adaptability, creativity.
(14:13):
I'm curious, how do yousee leaders guiding teams
through that transition,especially in remote settings?
Darren Murph (14:21):
Yeah.
This is a tough challengebecause it's not about the
ones and zeros at this point.
It's about the humanheart and the human mind.
And psychology is complicated,and so for people that have
made their careers out ofbeing great at doing a task
and then creating visibilityaround doing that task.
(14:41):
This is a bit of an unsettlingtime because now the Future
Prize is actually going tobe who is the most innovative
in discovering solutions,using new technologies that
enable people other thanthemselves to accomplish tasks.
And so I think what's happeninghere is the evolution that's
happening is really as simpleas what I just articulated,
(15:02):
but for the human mind, theyneed a leader to guide them
and coax them and honestlygive them some grace as they
go through this transition.
I would actually look to thegreat Charlie Munger who said,
if you show me the incentives,I'll show you the outcome.
And so for leaders who aretrying to galvanize a team
around doing things differently,think about the incentives.
(15:23):
If your current organizationis incentivized around tasks,
you can't just implement thismassive sea change around
wanting people to think abouttheir work differently and
yet not change the incentives.
And so as you're guidingpeople and coaching people
through the change, also goback to your total rewards
and ask yourself, Hey, dowe have the right systems in
place to make sure that we'reincentivizing people to go
(15:46):
along with this versus resist.
David Rice (15:48):
Some of your work, obviously you've talked
quite a bit about culture,right, and cultural shifts.
And I'm curious, when you talkabout incentives like that, do
we need a new sort of AI eraincentive models, maybe it's
profit sharing or anythingthat can sort of reduce fear
and encourage innovation?
Darren Murph (16:05):
Yeah.
This is a great example ofnot reinventing the wheel.
Speaking of Charlie Munger, hehas this wonderful example Xerox
many decades ago where theyintroduced a new model and a
year later they realized that itwas selling incredibly poorly.
And they thought,is it marketing?
Are we not conveying to themarket that the new model is
better than the old model?
(16:26):
But then they went andlooked at the sales incentive
structure and they realizedthat it had never been updated.
And so the sales team wasstill incentivized to make
more of selling the old unit.
So somehow they wereconvincing people that the
old model was even betterthan the new model in order
to align themselves with theincentives that were in place.
And so this is justquintessential sales
(16:46):
profit sharing.
And so when I look at leaderssaying, Hey, we want everyone
here to use AI to makethemselves 20% more efficient,
or to save the company 20%in whatever metric it is.
Have you considered giving someof that back to the individual?
This is a very simpleincentivized structure.
Companies do this allthe time with hackathons.
(17:07):
They bring all of theirengineers together for a week
and they say, whoever canbuild the next major iteration
of our app, you get a bonus.
And so it galvanizes a teamaround, Hey, now we are
aligned towards a goal.
Let's make progress.
It really is to me as simpleas consider profit sharing.
If a company or an individualor a team generates a 10 or
(17:28):
15% templatized, repeatabletype of efficiency gain, give
some of that back to them.
Whether that's time back intheir day or more flexibility
or a monetary bonus.
There's lots of ways thatyou can reward someone.
But give some of that back.
I don't think you shouldask your team to make all
of these gains for you,and then the organization
just absorbs all of it.
David Rice (17:50):
Yeah, I'd agree.
And I've seen alot of hackathons.
It's interesting too,'cause so much of what comes
outta those often strugglesto be operationalized.
And I also, maybe it's part ofeven that incentive structure
is figuring out how we'regoing to operationalize this.
That way it gets incentivizedeven outside of the engineering
team, I mean, in that example.
Darren Murph (18:09):
One other point that I would mention here and it
is a bit wild to think that thisis out of the box thinking, but
consider spinning up a dedicatedteam to find these gains.
I mean, imagine how ludicrous itwould be if you put out to the
entire organization, Hey, makeour company 20% more secure.
Do a lot of your own homeworkon security protocols of the
day and phishing and all ofthese things that barely have
(18:33):
anything to do with theirjob company is going to be
much more efficient if theyactually put the security team.
On that task.
And I would say that there'sprobably a lot of people in
your company right now that arejust huge personal fans of AI
and they are already spendinga lot of their personal time
becoming experts in this.
And so you may not need tonecessarily hire an external
(18:57):
expert or maybe partner withone that can foster a team
within your company, but thereis something to be said about
let the experts be the experts.
David Rice (19:06):
Absolutely.
It seems to me like AIadoption to this point.
It's been a case oftwo extremes, right?
Leaders who either let peoplego at it and they're sort of
just like off on their own,or they kind of over govern
AI use and they get veryconcerned with guardrails.
Right?
And I'm curious, whatdoes balance governance
look like that allowscreativity without chaos.
(19:30):
It suits whatever work modelyou're in, whether it's
remote or you know, you'rehybrid, that kind of thing.
Darren Murph (19:36):
I would take a two-pronged approach.
One would be laying out a clearvision of what are we as an
organization trying to achieve.
And then also create anincentive structure for people
to innovate and expand beyondwhat the leaders are currently
capable of thinking of.
So this kind of allows bothof those things to happen.
You do allow peopleto "go wild".
(19:58):
But you have an end in mind.
They are incentivizedaround specific metrics.
Maybe that'sefficiency, for example.
So at least they're goingwild toward a certain goal
that you've agreed on.
And then on the other side ofthat, you can consider that
govern around what are we tryingto achieve as an organization.
How does thisshape our strategy?
(20:19):
And then how can we cascadethat to everybody that works
here, give them some sort ofvision or a hope, some sort of
inkling of what the future mayhold, not just, Hey, let's get
better at AI, or let's makesure that we have AI because
everyone else is doing it andwe're falling for social proof.
So I think if you do thosetwo things in parallel, you'll
unlock the true innovators.
(20:40):
To run wild toward a directionthat you want them to, and for
everyone else, there's less fearinvolved because they can say,
ah, I see where we are goingas a team, and I understand
how AI, like any technologyis going to be one additional
thing that's going to getus closer to achieving that.
David Rice (20:58):
Yeah.
We've talked a lot in recentmonths about purpose, right?
And that sense of like,am I contributing to the
bigger goal and what amdoes what I'm doing matter.
You can communicate that,not just buy-in, but just
the commitment level toexecuting is so much higher.
Darren Murph (21:14):
It reminds me of a study that Google did,
I think it was over 15 yearsago, and they looked into the
five resilient dynamics ofan effective team, and one of
them was clarity and structure.
And I look at teams today, andI would say maybe that's the
most important one when you'rein a distributed environment.
(21:36):
If you give people clarityand structure on what are we
aiming for, it allows themto align their purpose with
the team and company purpose.
And so they feel less adriftand they're more motivated to
present their innovation tothe company if they know that
it's going in a direction thathas been clearly communicated.
And interestingly enough,this loops all the way
(21:56):
back to the beginning ofour conversation around.
Why have a writing culture?
Why write things down?
Why insist on rigoraround documentation?
Because that's where clarityand structure comes from.
And there's been so many studiesdone that show people thrive
on this, they love this, andif something is codified and
written down, now you have abaseline for making it better.
(22:18):
So even if it's imperfect,even if it's a work in
progress, things that arewritten down can be changed
and evolved with more input.
David Rice (22:26):
My final question for you is just sort of around,
you know, with AI handling thepolished and the routine, right?
It seems like spontaneous,imperfect, human moments,
they're kind of becoming adifferent currency almost
in the workplace, right?
So how do organizationsencourage humanity
in an increasingly AIdriven remote workplace?
Darren Murph (22:49):
So funny, right?
The "soft skills" are becomingall the more important when
AI can write your emails, likewhat's left for you to do.
But to me I think this unlocksa stance that I've had for many
years, which is increasinglyculture is going to be built
outside of the workplace.
Progressive organizationsare going to not only accept
(23:10):
that they're going to empowerthat, and then they're
gonna create avenues forthat culture to be funneled
back into the organization.
What does that mean?
It means that in a distributedworld where AI is potentially
a colleague, it is less likelythat we're going to get all
of our culture, all of oursocial impact from work.
And this is a big pill toswallow for people, leaders
(23:32):
who have always wanted tocontrol that experience,
but you have to let go.
An example that I'll share withyou is during the pandemic,
a lot of people came to meand they said, Hey, the Zoom
happy hour, it worked for thefirst time, but then no one
showed up for the second one.
How do we actually buildteam culture without forcing
people into a Zoom call?
And I said, well, if thathour is already a sunk cost,
(23:53):
just deploy everybody inyour organization out into
their local environment forthat same hour each week.
And just tell them to dosomething that's meaningful
to them, whether that'svolunteering at an orphanage
or volunteering for a foodbank or Habitat for Humanity.
I said, ask them to wearcompany swag, wear your
logo, and then maybe take aselfie of themselves while
(24:14):
they're engaged in that work.
And then when they come backto their next team meeting,
spend the first 10 minutestalking about what everyone did.
Share those selfies,share those aha moments.
That is real, genuine,authentic connection.
And so, although that culturewas actually built outside of
the workplace is responsiblefor building a system to
(24:35):
enable that to come back in.
And I would argue that is muchmore genuine and authentic, and
this is the opportunity that wehave to stop manufacturing fun
and actually enable people tobring that into the workplace.
David Rice (24:48):
Yeah, and when you're doing these
things right, purpose.
It takes care of itselfin a lot of ways.
Right?
Darren Murph (24:54):
When you let humans be humans, they really
enjoy being themselves.
David Rice (24:58):
Exactly.
Well, Darren, I wanna thank youfor coming on the show today.
It's been great asalways chatting with you.
Darren Murph (25:03):
Absolutely, man.
It's a pleasure.
And look, if any of yourlisteners are interested in
what I'm building, I have agreat team that would love
to get you to some of theareas that we've discussed.
And so reach out tome on LinkedIn or
darren@darrenmurph.com.
David Rice (25:16):
Excellent.
Thank you for joiningus today on The People
Managing People Podcast.
If today's conversationgave you a new perspective,
make sure to follow theshow on Apple Podcast,
Spotify, or whatever you useto listen to your podcast.
You can also find more practicalframeworks, case studies,
tools for modern leadership atpeoplemanagingpeople.com and
(25:38):
of course in our newsletter,so get signed up for that.
And until next time,build a writing culture.
A lot of people out there, they're
rushing to adopt AI.
What foundational workdo organizations need
to do before layering AIinto existing systems?
Darren Murph (00:10):
I see a lot of organizations rushing to
layer it over whatever theycurrently have, looking for
sparks of efficiency withoutstepping back and building a
great knowledge infrastructure,creating a one star
experience for all employees.
David Rice (00:25):
How does AI make documentation hygiene
even more essentialfor trust and adoption?
Darren Murph (00:30):
In a distributed world, you trust information
even more than people.
If AI leads you astray fouror five times in a row,
you're going to be much lesstrusting of that technology,
not just that day, butevery day going forward.
David Rice (00:44):
What specific lessons from distributed work
do you see kind of carryingover into how companies
will successfully adopt AI?
Darren Murph (00:52):
Two major first principles — transparency,
and asynchronous work flows.
The more that you've shiftedyour culture into a writing
culture, the better off youare now that we are moving from
the remote era to the AI era.
I would encourage leadersto take a step back and
make sure that you havethe right foundations in
project management andknowledge management.
David Rice (01:18):
Welcome to The People Managing People
Podcast — the show wherewe help leaders keep work
human in the era of AI.
I'm your host, David Rice.
And on today's show I'mjoined by Darren Murph.
Now you may know himas the go-to guy on
all things remote work.
He's the former head ofremote at GitLab and the
founder of Page 52 Consulting.
We're gonna be chatting abouthow AI is inherently a remote
(01:42):
work tool and how you can use itto build remote work cultures.
So Darren, welcome!
Darren Murph (01:46):
Hey, thanks for having me.
David Rice (01:47):
Alright, so just wanna jump right into it.
You know, a lot of people areout there, they're rushing
to adopt AI and of courseit's everybody's talking
like garbage in, garbage out.
That rule always applies.
Certainly applies here.
What foundational work doyou think organizations
need to do before layeringAI into existing systems,
especially when we think aboutremote work environments?
Darren Murph (02:10):
Yeah, this is a great question.
I see a lot of organizationsrushing to adopt AI and layer
it over whatever they currentlyhave, and then they're looking
for sparks of efficiencyor movement or some sort of
silver bullet for the future.
But I would encourage leadersto take a step back, take a
pause, and make sure that youhave the right foundations
(02:32):
in project managementand knowledge management.
And the knowledge oneis the most important.
So I wanna share twoexamples to kind of paint the
picture of what I mean here.
Imagine that your companyis a widget that anyone in
the world can buy on Amazon.
So it gets shipped to theirmailbox, they open it and
they shake it out, and yourwidget plops on the counter
and they say, how am Isupposed to operate this thing?
(02:55):
So then they look up inthe envelope and they shake
it some more expecting anoperating manual to fall out.
Now in the absence of anoperating manual, how do
you think people are goingto know how to use the
thing that you've built?
Well, a lot of trial and error.
This is kind of likeonboarding into most companies
right now that have notcodified the operating
rhythms of their company.
(03:15):
So what does this looklike in the age of AI?
Well, everything compounds,so if you have everything well
documented, that's going tocompound into AI having a lot of
great resources to extract from.
It compounds in theopposite direction too.
So imagine you're aticketing platform and
you sell concert tickets,and there's this upcoming
(03:35):
concert on your platform andtens of thousands of people
show up to buy tickets.
In addition to buyingtickets, they wanna use AI
to figure out what's the bestvalue for where I can sit.
And they also want to takethings into consideration,
like where is the sunlightgoing to be coming in at
the time of day that thisconcert is scheduled for?
And because I'm a baseplayer, I actually want to
(03:56):
know which seats should Ioptimize for based on where
the band will be set up.
Is the bass player going to beon stage left or stage right?
And many other things that youcould imagine AI helping with.
So imagine they show up to thisticketing site, but you, the
leader has built infrastructurethat has an old seating chart
that has an old incarnation ofthe band with the wrong layout.
(04:20):
Some of the seedsaren't even mapped yet.
So as a consumer trying to useAI to navigate this, you're
going to have an incrediblyfrustrating experience.
And it's likely that you'll notonly never come back to this
platform, but you'll find anyand all areas on the internet
to leave a one star review.
And so I worry that forleaders who are layering AI
(04:40):
now, without stepping backand building a great knowledge
infrastructure, you're actuallycreating a one star experience
for all of your employees.
David Rice (04:49):
That's a great analogy and I really liked,
you know, sort of thepicture that it paints.
And like remote work, youknow, this era has taught
companies a lot about theimportance of documentation
and single sources of truth.
But I'm curious, youknow, how does AI make
documentation, hygienemaybe even more essential
for trust and adoption?
Darren Murph (05:08):
We talk about trust as that's
the speed in which yourbusiness is going to run.
In a primarily co-locatedworld where most people go
to the same office to do worktogether, you really want to
be able to build trust with theother humans that you are in
close proximity to, but as theworld becomes more distributed.
You need to be ableto trust information
even more than people.
(05:29):
And this is especiallytrue in the age of AI.
I mean, you jokingly mentionedhallucinations, but seriously,
when you are engaging with AI,if it leads you astray four
or five times in a row, andyour reputation or the outcome
of a project depends on that.
You're going to be much lesstrusting of that technology,
not just that day, butevery day going forward.
(05:52):
And so, while documentationwas perhaps a nice to have
in the past, because youcould always use synchronous
meetings and verbalization topatch over communication gaps,
that's no longer true with AI.
You can't ask AI for input ona certain piece of information.
And then say whatever gaps youfind in the knowledge base,
(06:15):
go ask Tim and then come backwith your complete analysis.
He can't just phone Timup and fill in those gaps.
At least not yet.
And even if it could, Iwould say that's probably
not the most efficientway to scale information.
David Rice (06:28):
Well, you bring up an interesting point there.
Like there's a lot of baddata floating around in, I
would say, in the majority oforganizations, quite frankly.
And so if we're like looking atwhat information can you trust?
And what information canyou feed it and trust
that, I mean, what you'regonna get back is real.
I'd say we've got a lot ofwork to do on the back end
there to get ourselves ready.
(06:49):
Like how would you grademost organizations'
readiness in this area?
Darren Murph (06:54):
Great question.
So I work with organizationsall the time in trying to
build out this infrastructureand before we do any work, we
actually do an analysis andthe output of it puts them on
a curve and it never fails.
They think they're muchfurther along on the curve.
Then they actually are.
And so in most cases, theyare ready to adopt these
(07:15):
infrastructure improvements.
They recognize andsee the need of it.
They're not saying thatthis is unimportant, but
they have precious littleactually implemented.
And a telltale sign is do youtreat knowledge like a product?
Do you actually have someoneinternally that is either a
chief documentarian or a chieflibrarian, or do you have
a number of people that arerunning this as a product?
(07:38):
Think about securityin an organization.
This isn't somethingyou just wing.
Most organizations have achief security officer, or
they have systems in place.
They have whole teams inplace because you can't
assume that every employeejoining your company is going
to be a master of security.
The same is true withknowledge and documentation
unless you came up witha journalism degree.
(07:59):
Most people aren't trained inhow to think about taxonomy
and knowledge information, butin the age of AI, this is so
important within a company.
Most people who are doingChatGPT searches on the
internet, they can get greatresults because it's looking at
the entirety of the internet.
But when you scope thatdown to just what's
in your organization.
(08:20):
You can't go outsideof those walls.
It's on the company to saywe need to take knowledge as
seriously as we do security.
It's time to actually investin a knowledge base and put
some systems and potentiallypeople around building that out.
David Rice (08:35):
Yeah, it's interesting you
say that about org.
So it's like a lot of us, right?
We all think we're moreadvanced in our use of
AI than maybe we are.
So that's, it's veryfitting actually.
It extrapolates outto the org level.
I'm curious, the remote workwave that came with COVID,
it maybe was like the bestpreparation for this next era
of work that we could have had.
(08:55):
I'm wondering what specificlessons from distributed work
and that time that we're allforced into it, do you see,
kind of carrying over into howcompanies will successfully
adopt and deploy AI?
Darren Murph (09:07):
Two major first principles of remote
work that carry overwell are transparency and
asynchronous first workflows.
So companies quickly foundwhen they all went remote
that they had to be much moretransparent than before because
everyone needed informationand you couldn't all be in
the same place at all times.
And so you had to quicklyfigure out, how do we codify
(09:29):
all of this information that'sin our minds and make it
accessible and searchable?
This is paying dividends forcompanies that really invested
in that during COVID, now thatyou lay your AI on top of it.
Their output is much betterbecause they've already done
the upfront work of codifyingwhat was in people's minds.
The other part of this isasynchronous workflows.
(09:50):
I'm sure you've seen so muchdata over the pandemic of
the multiple hundreds ofpercentage point uptick in the
amount of teams meetings thatpeople engaged in, because
that was the only way thatthey knew to get work done,
and leaders quickly realizedthat this was not sustainable.
They implemented thingslike project management
tools, Asana, or Clickup.
(10:10):
These are great examples ofmoving work out of synchronous
meetings into tools thatare actually designed to
move large projects forward.
And so for organizations thatdid that, guess what all of
those projects that are codifiedin a tool instead of just being
out in the ether in a meeting.
Now AI can lookinto that as well.
(10:31):
So said another way.
The more that you've codified,the more that you've shifted
your culture into a writingculture, the better off you are.
Now that we are moving fromthe remote era to the AI era.
David Rice (10:45):
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It's so interesting every timeI hear you talk about this
and talking about a writingculture, it's funny 'cause I
think back to like, you know,when I was coming outta school
and people were like, ah, youknow, your skillset might not
be that valuable in 10 years.
(11:29):
And it turns out actually itturned out to be quite valuable.
It is actually sort of thekey, I think, to this next
chapter, which I'm grateful for.
Even companies callingemployees back to the office,
you know, a lot of 'em arestill distributed across
different cities and time zones.
How does AI kinda reinforcethe need for distributed
(11:50):
friendly practices?
Because even in this newreality, even when we're
co-located, we're stillseparate so much of the time,
hybrid in different cities.
So I, I'm curious whatyou think about that.
Darren Murph (12:01):
Yeah, look at it this way.
If you remove the physicaloffice from most multinationals,
business would go on, but ifyou removed the internet from
most multinationals, theirentire business would grind to
a halt probably within a week.
So which one is most important?
And so I chuckle a bit at thisbecause even organizations
that are making a huge dealabout enforcing and mandating
(12:24):
a return to office, unlessthey literally have one office
where everyone is literallyon one floor, they need to
embrace remote first principles.
There's been researchthat has done that.
Even if you're in oneskyscraper, the employee's
on floor seven and theemployee's on floor 41 almost
never engage with each otherin person, so they might
(12:45):
as well be oceans away.
And for most multinationals,they have multiple offices and
these span different culturesand languages and time zones.
And so whether they wantto identify as such or
not, the vast majority oforganizations in 2025 and
beyond are distributed.
And so ignoring thatreality just means that
they're falling further andfurther behind the curve.
(13:08):
When you think about how peopleengage with AI, even outside of
their organization, it is nota physical place that they go.
They don't get in a vehicle anddrive down to the local Lions
club in order to engage with AI.
They just use whateverdevice is in front of them.
And so the further that wego along, I would posit that
we are going to become a moredistributed working society,
(13:29):
not a less distributedworking society and AI is
only furthering that reality.
David Rice (13:35):
I feel like that's like the premise for a movie.
You know, you go down tothe Lions Club to like
talk to the AI, it's likezoltar in big, you know?
Darren Murph (13:42):
Yeah.
It's the new versionof the payphone.
Like we actually have to drivesomewhere to make a phone call.
I don't know, I think there'sa another season of The Wire.
David Rice (13:52):
I love it.
Some employees, when wethink about AI adoption, some
folks are fearing it, right?
And they see it as sortof like they're training
their replacement.
And I think it's a verylogical, sort of valid
concern for a lot of folks.
It seems to be sort of, we're inthis phase of shifting our value
away from tasks toward learning,adaptability, creativity.
(14:13):
I'm curious, how do yousee leaders guiding teams
through that transition,especially in remote settings?
Darren Murph (14:21):
Yeah.
This is a tough challengebecause it's not about the
ones and zeros at this point.
It's about the humanheart and the human mind.
And psychology is complicated,and so for people that have
made their careers out ofbeing great at doing a task
and then creating visibilityaround doing that task.
(14:41):
This is a bit of an unsettlingtime because now the Future
Prize is actually going tobe who is the most innovative
in discovering solutions,using new technologies that
enable people other thanthemselves to accomplish tasks.
And so I think what's happeninghere is the evolution that's
happening is really as simpleas what I just articulated,
(15:02):
but for the human mind, theyneed a leader to guide them
and coax them and honestlygive them some grace as they
go through this transition.
I would actually look to thegreat Charlie Munger who said,
if you show me the incentives,I'll show you the outcome.
And so for leaders who aretrying to galvanize a team
around doing things differently,think about the incentives.
(15:23):
If your current organizationis incentivized around tasks,
you can't just implement thismassive sea change around
wanting people to think abouttheir work differently and
yet not change the incentives.
And so as you're guidingpeople and coaching people
through the change, also goback to your total rewards
and ask yourself, Hey, dowe have the right systems in
place to make sure that we'reincentivizing people to go
(15:46):
along with this versus resist.
David Rice (15:48):
Some of your work, obviously you've talked
quite a bit about culture,right, and cultural shifts.
And I'm curious, when you talkabout incentives like that, do
we need a new sort of AI eraincentive models, maybe it's
profit sharing or anythingthat can sort of reduce fear
and encourage innovation?
Darren Murph (16:05):
Yeah.
This is a great example ofnot reinventing the wheel.
Speaking of Charlie Munger, hehas this wonderful example Xerox
many decades ago where theyintroduced a new model and a
year later they realized that itwas selling incredibly poorly.
And they thought,is it marketing?
Are we not conveying to themarket that the new model is
better than the old model?
(16:26):
But then they went andlooked at the sales incentive
structure and they realizedthat it had never been updated.
And so the sales team wasstill incentivized to make
more of selling the old unit.
So somehow they wereconvincing people that the
old model was even betterthan the new model in order
to align themselves with theincentives that were in place.
And so this is justquintessential sales
(16:46):
profit sharing.
And so when I look at leaderssaying, Hey, we want everyone
here to use AI to makethemselves 20% more efficient,
or to save the company 20%in whatever metric it is.
Have you considered giving someof that back to the individual?
This is a very simpleincentivized structure.
Companies do this allthe time with hackathons.
(17:07):
They bring all of theirengineers together for a week
and they say, whoever canbuild the next major iteration
of our app, you get a bonus.
And so it galvanizes a teamaround, Hey, now we are
aligned towards a goal.
Let's make progress.
It really is to me as simpleas consider profit sharing.
If a company or an individualor a team generates a 10 or
(17:28):
15% templatized, repeatabletype of efficiency gain, give
some of that back to them.
Whether that's time back intheir day or more flexibility
or a monetary bonus.
There's lots of ways thatyou can reward someone.
But give some of that back.
I don't think you shouldask your team to make all
of these gains for you,and then the organization
just absorbs all of it.
David Rice (17:50):
Yeah, I'd agree.
And I've seen alot of hackathons.
It's interesting too,'cause so much of what comes
outta those often strugglesto be operationalized.
And I also, maybe it's part ofeven that incentive structure
is figuring out how we'regoing to operationalize this.
That way it gets incentivizedeven outside of the engineering
team, I mean, in that example.
Darren Murph (18:09):
One other point that I would mention here and it
is a bit wild to think that thisis out of the box thinking, but
consider spinning up a dedicatedteam to find these gains.
I mean, imagine how ludicrous itwould be if you put out to the
entire organization, Hey, makeour company 20% more secure.
Do a lot of your own homeworkon security protocols of the
day and phishing and all ofthese things that barely have
(18:33):
anything to do with theirjob company is going to be
much more efficient if theyactually put the security team.
On that task.
And I would say that there'sprobably a lot of people in
your company right now that arejust huge personal fans of AI
and they are already spendinga lot of their personal time
becoming experts in this.
And so you may not need tonecessarily hire an external
(18:57):
expert or maybe partner withone that can foster a team
within your company, but thereis something to be said about
let the experts be the experts.
David Rice (19:06):
Absolutely.
It seems to me like AIadoption to this point.
It's been a case oftwo extremes, right?
Leaders who either let peoplego at it and they're sort of
just like off on their own,or they kind of over govern
AI use and they get veryconcerned with guardrails.
Right?
And I'm curious, whatdoes balance governance
look like that allowscreativity without chaos.
(19:30):
It suits whatever work modelyou're in, whether it's
remote or you know, you'rehybrid, that kind of thing.
Darren Murph (19:36):
I would take a two-pronged approach.
One would be laying out a clearvision of what are we as an
organization trying to achieve.
And then also create anincentive structure for people
to innovate and expand beyondwhat the leaders are currently
capable of thinking of.
So this kind of allows bothof those things to happen.
You do allow peopleto "go wild".
(19:58):
But you have an end in mind.
They are incentivizedaround specific metrics.
Maybe that'sefficiency, for example.
So at least they're goingwild toward a certain goal
that you've agreed on.
And then on the other side ofthat, you can consider that
govern around what are we tryingto achieve as an organization.
How does thisshape our strategy?
(20:19):
And then how can we cascadethat to everybody that works
here, give them some sort ofvision or a hope, some sort of
inkling of what the future mayhold, not just, Hey, let's get
better at AI, or let's makesure that we have AI because
everyone else is doing it andwe're falling for social proof.
So I think if you do thosetwo things in parallel, you'll
unlock the true innovators.
(20:40):
To run wild toward a directionthat you want them to, and for
everyone else, there's less fearinvolved because they can say,
ah, I see where we are goingas a team, and I understand
how AI, like any technologyis going to be one additional
thing that's going to getus closer to achieving that.
David Rice (20:58):
Yeah.
We've talked a lot in recentmonths about purpose, right?
And that sense of like,am I contributing to the
bigger goal and what amdoes what I'm doing matter.
You can communicate that,not just buy-in, but just
the commitment level toexecuting is so much higher.
Darren Murph (21:14):
It reminds me of a study that Google did,
I think it was over 15 yearsago, and they looked into the
five resilient dynamics ofan effective team, and one of
them was clarity and structure.
And I look at teams today, andI would say maybe that's the
most important one when you'rein a distributed environment.
(21:36):
If you give people clarityand structure on what are we
aiming for, it allows themto align their purpose with
the team and company purpose.
And so they feel less adriftand they're more motivated to
present their innovation tothe company if they know that
it's going in a direction thathas been clearly communicated.
And interestingly enough,this loops all the way
(21:56):
back to the beginning ofour conversation around.
Why have a writing culture?
Why write things down?
Why insist on rigoraround documentation?
Because that's where clarityand structure comes from.
And there's been so many studiesdone that show people thrive
on this, they love this, andif something is codified and
written down, now you have abaseline for making it better.
(22:18):
So even if it's imperfect,even if it's a work in
progress, things that arewritten down can be changed
and evolved with more input.
David Rice (22:26):
My final question for you is just sort of around,
you know, with AI handling thepolished and the routine, right?
It seems like spontaneous,imperfect, human moments,
they're kind of becoming adifferent currency almost
in the workplace, right?
So how do organizationsencourage humanity
in an increasingly AIdriven remote workplace?
Darren Murph (22:49):
So funny, right?
The "soft skills" are becomingall the more important when
AI can write your emails, likewhat's left for you to do.
But to me I think this unlocksa stance that I've had for many
years, which is increasinglyculture is going to be built
outside of the workplace.
Progressive organizationsare going to not only accept
(23:10):
that they're going to empowerthat, and then they're
gonna create avenues forthat culture to be funneled
back into the organization.
What does that mean?
It means that in a distributedworld where AI is potentially
a colleague, it is less likelythat we're going to get all
of our culture, all of oursocial impact from work.
And this is a big pill toswallow for people, leaders
(23:32):
who have always wanted tocontrol that experience,
but you have to let go.
An example that I'll share withyou is during the pandemic,
a lot of people came to meand they said, Hey, the Zoom
happy hour, it worked for thefirst time, but then no one
showed up for the second one.
How do we actually buildteam culture without forcing
people into a Zoom call?
And I said, well, if thathour is already a sunk cost,
(23:53):
just deploy everybody inyour organization out into
their local environment forthat same hour each week.
And just tell them to dosomething that's meaningful
to them, whether that'svolunteering at an orphanage
or volunteering for a foodbank or Habitat for Humanity.
I said, ask them to wearcompany swag, wear your
logo, and then maybe take aselfie of themselves while
(24:14):
they're engaged in that work.
And then when they come backto their next team meeting,
spend the first 10 minutestalking about what everyone did.
Share those selfies,share those aha moments.
That is real, genuine,authentic connection.
And so, although that culturewas actually built outside of
the workplace is responsiblefor building a system to
(24:35):
enable that to come back in.
And I would argue that is muchmore genuine and authentic, and
this is the opportunity that wehave to stop manufacturing fun
and actually enable people tobring that into the workplace.
David Rice (24:48):
Yeah, and when you're doing these
things right, purpose.
It takes care of itselfin a lot of ways.
Right?
Darren Murph (24:54):
When you let humans be humans, they really
enjoy being themselves.
David Rice (24:58):
Exactly.
Well, Darren, I wanna thank youfor coming on the show today.
It's been great asalways chatting with you.
Darren Murph (25:03):
Absolutely, man.
It's a pleasure.
And look, if any of yourlisteners are interested in
what I'm building, I have agreat team that would love
to get you to some of theareas that we've discussed.
And so reach out tome on LinkedIn or
darren@darrenmurph.com.
David Rice (25:16):
Excellent.
Thank you for joiningus today on The People
Managing People Podcast.
If today's conversationgave you a new perspective,
make sure to follow theshow on Apple Podcast,
Spotify, or whatever you useto listen to your podcast.
You can also find more practicalframeworks, case studies,
tools for modern leadership atpeoplemanagingpeople.com and
(25:38):
of course in our newsletter,so get signed up for that.
And until next time,build a writing culture.
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